利用多传感器高维时域特征扩展图诊断泵车防水阀故障

IF 2.1 4区 工程技术 Advances in Mechanical Engineering Pub Date : 2024-04-29 DOI:10.1177/16878132241245894
Rui Zhang, Jiyan Yi, Hanlin Guan, Yao Xiao, Wangfang Tao, Yan Ren
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引用次数: 0

摘要

大多数泵车的主缸都装有防水阀,其作用是防止油箱中的水进入主缸。一旦防水阀失效,主缸的防水阀只能靠 BS 密封件支撑(该密封件极易失效),从而导致油液乳化,污染液压系统。因此,我们提出了一种将多传感器高维时域特征扩展图(MHTFEM)与注意力卷积胶囊网络(ACCN)相结合的故障诊断方法。在该方法中,首先对所有传感器获取的原始振动信号进行预处理,生成高维特征矩阵。然后将不同的高维特征矩阵拼接、扩展并生成灰度图像,接着随机划分训练集和测试集。最后,将训练集引入 ACCN 进行训练,将测试集引入网络模型进行故障类型识别。为证实该方法在防水阀故障诊断方面的有效性,建立了一个测试台。这为实现工程机械的智能故障诊断提供了一种方法,以确保其可靠性。
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Fault diagnosis of pump truck waterproof valves using multi-sensor high-dimensional time-domain feature expansion map
The master cylinder of most pump trucks is equipped with a waterproof valve, whose purpose is to prevent water from the tank from entering the master cylinder. Once waterproof valve fails to failure, the waterproof valve at the main cylinder can only be supported by a BS seal (this seal is very easy to fail), which results in oil emulsification and pollution of the hydraulic system. Therefore, a fault diagnosis method combining a multi-sensor high-dimensional time-domain feature expansion map (MHTFEM) with an attentional convolutional capsule network (ACCN) is proposed. In this method, the raw vibration signals acquired by all sensors are first preprocessed to generate a high-dimensional feature matrix. Then the different high-dimensional feature matrices are stitched, expanded and generated into grayscale images, followed by randomly dividing the training set and the testing set. Finally, the training set is brought into the ACCN for training and the testing set is brought into the network model for fault type identification. A test bench was built to confirm the effectiveness of the method for waterproof valve fault diagnosis. This provides a method to achieve intelligent fault diagnosis of construction machinery to ensure its reliability.
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来源期刊
Advances in Mechanical Engineering
Advances in Mechanical Engineering Engineering-Mechanical Engineering
自引率
4.80%
发文量
353
期刊介绍: Advances in Mechanical Engineering (AIME) is a JCR Ranked, peer-reviewed, open access journal which publishes a wide range of original research and review articles. The journal Editorial Board welcomes manuscripts in both fundamental and applied research areas, and encourages submissions which contribute novel and innovative insights to the field of mechanical engineering
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